Tree-based Pipeline Optimization Tool Research Articles

Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning

AbstractSolution styrene‐butadiene rubber (SSBR) finds wide applications in high performance tire design and various other fields. This study aims to create a quantitative structure–property relationship (QSPR) model linking SSBR's glass transition temperature (Tg) to its structural properties. A dataset of 68 sets of data from published literature was compiled to develop a predictive machine learning model for SSBR's structural design and synthesis using small sample sizes. To tackle small sample sizes, a framework combining generative adversarial networks (GAN) and the Tree‐based Pipeline Optimization Tool (TPOT) is proposed. GAN is first used to generate additional samples that mirror the original dataset's distribution, expanding the dataset. The TPOT is then applied to automatically find the best model and parameter combinations, creating an optimal predictive model for the mixed dataset. Experimental results show that using GAN to enlarge the dataset and TPOT regression models significantly enhances model performance, increasing the R2 value from 0.745 to 0.985 and decreasing the RMSE from 7.676 to 1.569. The proposed GAN–TPOT framework demonstrates the potential of combining generative models with automated machine learning to improve materials science research. This combination accelerates research and development processes, enhances prediction and design accuracy, and introduces new perspectives and possibilities for the field.

Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques

Lung cancer remains a leading cause of cancer-related deaths globally. Early and accurate detection is crucial for improving patient outcomes. Traditional methods, relying on manual interpretation of medical images, are time-consuming and prone to errors. Deep learning, particularly convolutional neural networks (CNNs), offers an automated alternative capable of learning intricate patterns from medical images. However, previous deep learning models for lung cancer detection have faced challenges such as limited data, inadequate feature extraction, interpretability issues, and susceptibility to data variability. This paper presents a novel deep learning methodology that addresses these limitations. Our approach leverages expansive datasets, incorporates advanced feature extraction techniques, improves interpretability, and accommodates the diverse nature of lung cancer. Specifically, we develop dedicated models for both chest X-ray and CT images utilizing publicly available datasets from Kaggle. Through the integration of feature selection and model selection techniques—such as employing a genetic algorithm in conjunction with the tree-based pipeline optimization tool (TPOT)—we achieved remarkable accuracy. Our X-ray model attains an overall accuracy of 95.47%, the CT model achieves an accuracy of 98.70%, and the combined model achieves an impressive overall accuracy of 98.93%. Our methodology significantly enhances the performance and efficiency of lung cancer detection and is a valuable tool for early diagnosis and intervention.

Machine learning-based prediction of contralateral knee osteoarthritis development using the Osteoarthritis Initiative and the Multicenter Osteoarthritis Study dataset.

Having osteoarthritis in one knee is reported as an independent risk factor for developing contralateral knee osteoarthritis (KOA). However, no study has been designed to predict the development of contralateral KOA (cKOA). The authors hypothesized that specific risk factors for cKOA development exist and that it could be accurately predicted with the assistance of machine learning. KOA was defined using the Kellgren-Lawrence grade (KLG) of 2 or higher. Data from 1353 unilateral KOA patients (900 from the Osteoarthritis Initiative [OAI] and 453 from the Multicenter Osteoarthritis Study [MOST]) over 4-5 years of follow-up were examined. The risk factors for cKOA development were analyzed, and a machine learning model was developed to predict cKOA using OAI as the development data set and MOST as the test data set. cKOA developed in 172 (19.1%) and 178 (39.3%) of the patients (OAI and MOST, respectively) over a period of 4-5 years. A machine learning model was developed using the Tree-based Pipeline Optimization Tool algorithm. This model utilized nine variables, including baseline lateral joint space narrowing grade of the contralateral knee (odds ratio 4.475). The area under the curve of the receiver operating characteristics curve, along with accuracy, precision, and F1-score, were recorded as 0.69, 0.60, 0.50, and 0.58, respectively, in the test data set. The development of cKOA could be effectively predicted using a limited number of variables through machine learning. Surgeons should consider the development of cKOA in patients with identified risk factors when managing KOA patients.

A Comparative Analysis of Automated Machine Learning Tools: A Use Case for Autism Spectrum Disorder Detection

Automated Machine Learning (AutoML) enhances productivity and efficiency by automating the entire process of machine learning model development, from data preprocessing to model deployment. These tools are accessible to users with varying levels of expertise and enable efficient, scalable, and accurate classification across different applications. This paper evaluates two popular AutoML tools, the Tree-Based Pipeline Optimization Tool (TPOT) version 0.10.2 and Konstanz Information Miner (KNIME) version 5.2.5, comparing their performance in a classification task. Specifically, this work analyzes autism spectrum disorder (ASD) detection in toddlers as a use case. The dataset for ASD detection was collected from various rehabilitation centers in Pakistan. TPOT and KNIME were applied to the ASD dataset, with TPOT achieving an accuracy of 85.23% and KNIME achieving 83.89%. Evaluation metrics such as precision, recall, and F1-score validated the reliability of the models. After selecting the best models with optimal accuracy, the most important features for ASD detection were identified using these AutoML tools. The tools optimized the feature selection process and significantly reduced diagnosis time. This study demonstrates the potential of AutoML tools and feature selection techniques to improve early ASD detection and outcomes for affected children and their families.

Predictive business process monitoring with AutoML for next activity prediction

Process mining is an emerging research field which deals with discovering, monitoring and improving business processes by analyzing and mining data in the form of event logs. Event logs can be extracted by most of the existing enterprise information systems. Predictive business process monitoring is a sub-field of process mining and deals with predictive analytics models on event log data that incorporate Machine Learning (ML) algorithms and deal with various objectives of process instances, such as: next activity, remaining time, costs, and risks. Existing research works on predictions about next activities are scarce. At the same time, Automated Machine Learning (AutoML) has not been investigated in the predictive business process monitoring domain. Therefore, based on its promising results in other domains and type of data, we propose an approach for next activity prediction based on AutoML, and specifically on the Tree-Based Pipeline Optimization Tool (TPOT) method for AutoML. The evaluation results demonstrate that automating the design and optimization of ML pipelines without the need for human intervention, apart from making accessible ML to non-ML experts (in this case, the process owners and the business analysts), also provides higher prediction accuracy comparing to other approaches in the literature.

Machine learning-assisted rapid determination for traditional Chinese Medicine Constitution

The aim of this study was to develop a machine learning-assisted rapid determination methodology for traditional Chinese Medicine Constitution. Based on the Constitution in Chinese Medicine Questionnaire (CCMQ), the most applied diagnostic instrument for assessing individuals’ constitutions, we employed automated supervised machine learning algorithms (i.e., Tree-based Pipeline Optimization Tool; TPOT) on all the possible item combinations for each subscale and an unsupervised machine learning algorithm (i.e., variable clustering; varclus) on the whole scale to select items that can best predict body constitution (BC) classifications or BC scores. By utilizing subsets of items selected based on TPOT and corresponding machine learning algorithms, the accuracies of BC classifications prediction ranged from 0.819 to 0.936, with the root mean square errors of BC scores prediction stabilizing between 6.241 and 9.877. Overall, the results suggested that the automated machine learning algorithms performed better than the varclus algorithm for item selection. Additionally, based on an automated machine learning item selection procedure, we provided the top three ranked item combinations with each possible subscale length, along with their corresponding algorithms for predicting BC classification and severity. This approach could accommodate the needs of different practitioners in traditional Chinese medicine for rapid constitution determination.

Machine learning computational model to predict lung cancer using electronic medical records

BackgroundLung cancer (LC) screening using low-dose computed tomography (CT) is recommended according to standard risk criteria or personalized risk calculators. Machine learning (ML) models that can predict disease risk are an emerging method in medicine for identifying hidden associations that are personally unique. Materials and methodsUsing the tree-based pipeline optimization tool (TPOT), we developed an ML-based model, which is an ensemble of the Random Forest and XGboost models, based on known risk factors for LC, as part of a larger trial for ML prediction using electronic medical records and chest CT. We used data from patients with LC vs. controls (1:2) of patients aged ≥ 35 years. We developed a model for all LC patients as well as for patients with and without a smoking background. We included age, gender, body mass index (BMI), smoking history, socioeconomic status (SES), history of chronic obstructive pulmonary disease (COPD)/emphysema/chronic bronchitis (CB), interstitial lung disease (ILD)/pulmonary fibrosis (PF), and family history of LC. ResultsOf the 4076 patients, 1428 (35 %) were in the LC group and 2648 (65 %) were in the control group. For the entire study population, our model achieved an accuracy of 71.2 %, with a sensitivity of 69 % and a positive predictive value (PPV) of 74 %. Higher accuracy was achieved for the two subgroups. An accuracy of 74.8 % (sensitivity 72 %, PPV 76 %) and 73.0 % (sensitivity 76 %, PPV 72 %) was achieved for the smoking and never-smoking cohorts, respectively. For the entire population and smoker cohort, COPD/emphysema/CB were the most important contributors, followed by BMI and age, while in the never-smoking cohort, BMI, age and SES were the most important contributors. ConclusionKnown risk factors for LC could be used in ML models to modestly predict LC. Further studies are needed to confirm these results in new patients and to improve them.

Automated machine learning for fabric quality prediction: a comparative analysis.

The enhancement of fabric quality prediction in the textile manufacturing sector is achieved by utilizing information derived from sensors within the Internet of Things (IoT) and Enterprise Resource Planning (ERP) systems linked to sensors embedded in textile machinery. The integration of Industry 4.0 concepts is instrumental in harnessing IoT sensor data, which, in turn, leads to improvements in productivity and reduced lead times in textile manufacturing processes. This study addresses the issue of imbalanced data pertaining to fabric quality within the textile manufacturing industry. It encompasses an evaluation of seven open-source automated machine learning (AutoML) technologies, namely FLAML (Fast Lightweight AutoML), AutoViML (Automatically Build Variant Interpretable ML models), EvalML (Evaluation Machine Learning), AutoGluon, H2OAutoML, PyCaret, and TPOT (Tree-based Pipeline Optimization Tool). The most suitable solutions are chosen for certain circumstances by employing an innovative approach that finds a compromise among computational efficiency and forecast accuracy. The results reveal that EvalML emerges as the top-performing AutoML model for a predetermined objective function, particularly excelling in terms of mean absolute error (MAE). On the other hand, even with longer inference periods, AutoGluon performs better than other methods in measures like mean absolute percentage error (MAPE), root mean squared error (RMSE), and r-squared. Additionally, the study explores the feature importance rankings provided by each AutoML model, shedding light on the attributes that significantly influence predictive outcomes. Notably, sin/cos encoding is found to be particularly effective in characterizing categorical variables with a large number of unique values. This study includes useful information about the application of AutoML in the textile industry and provides a roadmap for employing Industry 4.0 technologies to enhance fabric quality prediction. The research highlights the importance of striking a balance between predictive accuracy and computational efficiency, emphasizes the significance of feature importance for model interpretability, and lays the groundwork for future investigations in this field.

Deep Heterogeneous AutoML Trend Prediction Model for Algorithmic Trading in the USD/COP Colombian FX Market Through Limit Order Book (LOB)

This study presents a novel and competitive approach for algorithmic trading in the Colombian US dollar inter-bank market (SET-FX). At the core of this strategy is an advanced predictive model, developed using the Tree-based Pipeline Optimization Tool (TPOT). TPOT, an automated machine learning platform based on strongly-typed genetic programming, incorporates the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This multi-objective evolutionary algorithm is instrumental in identifying machine learning models that strike an optimal balance between high accuracy and low complexity, thereby advancing the field of predictive modeling in financial markets.

An AutoML-based approach for automatic traffic incident detection in smart cities

In the realm of modern urban mobility, automatic incident detection is a critical element of intelligent transportation systems (ITS), since the ability to promptly identify unexpected events allows for quick implementation of preventive measures and efficient response to the situations as they arise. With the growing availability of traffic data, Machine Learning (ML) has become a vital tool for enhancing traditional incident detection methods. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process; however the application of AutoML for incident detection has not been widely explored in scientific research In this paper, we propose and apply an AutoML-based methodology for traffic incident detection and compare it with state-ofthe-art ML approaches. Our approach integrates data preprocessing with AutoML, and uses Tree-based Pipeline Optimization Tool (TPOT) to refine the process from raw data to prediction. We have tested the efficiency of our approach in two major European cities, Athens and Antwerp. Finally, we present the limitations of our work and outline recommendations for application of AutoML in the incident detection task and potentially in other domains.

Optimized machine learning models for predicting crown convergence of plateau mountain tunnels

The precise prediction of tunnel convergence holds significant importance in ensuring the safety and efficiency of tunnel construction. This study involves the construction of seven machine learning (ML) models that are deemed reliable for predicting crown convergence (CC) of plateau mountain tunnels. These models include K-nearest neighbour (KNN), support vector regression (SVR), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and automated machine learning model (Auto-ML). The Bayesian optimization (BO) technique and the Tree-based pipeline optimization tool (TPOT) were employed to optimize the hyperparameters of non-automated machine learning models and the automated machine learning model, separately. A total of 2,734 samples of crown convergence monitoring data were collected for training and testing the models. The evaluation metrics employed to assess the performance of the models’ predictions included the determination coefficient (R2), mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE). The XGBoost model demonstrated the best prediction performance with the R2, MAPE, MAE, and RMSE of 0.9887, 0.1271, 0.7759, and 1.1845 on test set. The XGBoost, CatBoost, and Auto-ML models were utilized to predict the crown convergence monitoring curves of four sections of Huzhubeishan Tunnel in Qinghai Province, China. Notably, the predicted curves exhibited a significant level of concordance with the actual monitoring curves. The results of Shapley Additive Explanations (SHAP) suggested that time, tunnel depth ratio and groundwater table were more important than other input variables. The XGBoost model showed the best robustness of prediction performance across varying dataset sizes for seven distinct machine learning models.

Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses

ObjectivesTo develop a radiomics model based on diffusion-weighted imaging (DWI) utilizing automated machine learning method to differentiate cerebral cystic metastases from brain abscesses.Materials and methodsA total of 186 patients with cerebral cystic metastases (n = 98) and brain abscesses (n = 88) from two clinical institutions were retrospectively included. The datasets (129 from institution A) were randomly portioned into separate 75% training and 25% internal testing sets. Radiomics features were extracted from DWI images using two subregions of the lesion (cystic core and solid wall). A thorough image preprocessing method was applied to DWI images to ensure the robustness of radiomics features before feature extraction. Then the Tree-based Pipeline Optimization Tool (TPOT) was utilized to search for the best optimized machine learning pipeline, using a fivefold cross-validation in the training set. The external test set (57 from institution B) was used to evaluate the model’s performance.ResultsSeven distinct TPOT models were optimized to distinguish between cerebral cystic metastases and abscesses either based on different features combination or using wavelet transform. The optimal model demonstrated an AUC of 1.00, an accuracy of 0.97, sensitivity of 1.00, and specificity of 0.93 in the internal test set, based on the combination of cystic core and solid wall radiomics signature using wavelet transform. In the external test set, this model reached 1.00 AUC, 0.96 accuracy, 1.00 sensitivity, and 0.93 specificity.ConclusionThe DWI-based radiomics model established by TPOT exhibits a promising predictive capacity in distinguishing cerebral cystic metastases from abscesses.

Creating optimized machine learning pipelines for PV power generation forecasting using the grid search and tree-based pipeline optimization tool

Creating optimized machine learning pipelines for PV power generation forecasting using the grid search and tree-based pipeline optimization tool

FLOW PATTERN PREDICTION IN HORIZONTAL AND INCLINED PIPES USING TREE-BASED AUTOMATED MACHINE LEARNING

In the oil and gas industry, understanding two-phase (gas-liquid) flow is pivotal, as it directly influences equipment design, quality control, and operational efficiency. Flow pattern determination is thus fundamental to industrial engineering and management. This study utilizes the Tree-based Pipeline Optimization Tool (TPOT), an Automated Machine Learning (AutoML) framework that employs genetic programming, in obtaining the best machine learning model for a provided dataset. This paper presents the design of flow pattern prediction models using the TPOT. The TPOT was applied to predict flow patterns in 2.5 cm and 5.1 cm diameter pipes, using datasets from existing literature. The datasets went through handling of imbalanced data, standardization, and one-hot encoding as data preparation techniques before being fed into TPOT. The models designed for the 2.5 cm and 5.1 cm datasets were named as FPTL_TPOT_2.5 and FPTL_TPOT_5.1, respectively. A comparative analysis of these models alongside other standard supervised machine learning models and similar state-of-the-art similar two-phase flow prediction models was carried out and the insights on the performance of these TPOT designed models were discussed. The results demonstrated that models designed with TPOT achieve remarkable accuracy, scoring 97.66% and 98.09%, for the 2.5 cm and 5.1 cm datasets respectively. Furthermore, the FPTL_TPOT_2.5 and FPTL_TPOT_5.1 models outperformed other counterpart machine learning models in terms of performance, underscoring TPOT’s effectiveness in designing machine learning models for flow pattern prediction. The findings of this research carry significant implications for enhancing efficiency and optimizing industrial processes in the oil and gas sector.

Inference of social cognition in schizophrenia patients with neurocognitive domains and neurocognitive tests using automated machine learning

AimIt has been suggested that single neurocognitive domain or neurocognitive test can be used to determine the overall cognitive function in schizophrenia using machine learning algorithms. It is unknown whether social cognition in schizophrenia patients can be estimated with machine learning based on neurocognitive domains or neurocognitive tests. MethodsTo predict social cognition in schizophrenia, we applied an automated machine learning (AutoML) framework resulting from the analysis of predictive factors such as six neurocognitive domain scores and nine neurocognitive test scores of 380 schizophrenia patients in the Taiwanese population. Four clinical parameters (i.e., age, gender, subgroup, and education) were also used as predictive factors. We utilized an AutoML framework called Tree-based Pipeline Optimization Tool (TPOT) to generate predictive pipelines automatically. ResultsThe analysis revealed that all neurocognitive domains and tests except the reasoning and problem solving domain/test showed significant associations with social cognition. In addition, a TPOT-generated pipeline can best predict social cognition in schizophrenia using seven predictive factors, including five neurocognitive domains (i.e., speed of processing, sustained attention, working memory, verbal learning and memory, and visual learning and memory) and two clinical parameters (i.e., age and gender). This predictive pipeline consists of machine learning algorithms such as function transformers, an approximate feature map, independent component analysis, and linear regression. ConclusionThe study indicates that an AutoML framework such as TPOT may provide a promising way to produce truly effective machine learning pipelines for predicting social cognition in schizophrenia using neurocognitive domains and/or neurocognitive tests.

Machine learning approaches to predict and detect early-onset of digital dermatitis in dairy cows using sensor data.

The present study aimed to employ machine learning algorithms based on sensor behavior data for (1) early-onset detection of digital dermatitis (DD) and (2) DD prediction in dairy cows. Our machine learning model, which was based on the Tree-Based Pipeline Optimization Tool (TPOT) automatic machine learning method, for DD detection on day 0 of the appearance of the clinical signs has reached an accuracy of 79% on the test set, while the model for the prediction of DD 2 days prior to the appearance of the first clinical signs, which was a combination of K-means and TPOT, has reached an accuracy of 64%. The proposed machine learning models have the potential to help achieve a real-time automated tool for monitoring and diagnosing DD in lactating dairy cows based on sensor data in conventional dairy barn environments. Our results suggest that alterations in behavioral patterns can be used as inputs in an early warning system for herd management in order to detect variances in the health and wellbeing of individual cows.

The possibility of rainfall nowcasting using atmospheric electric field

The possibility of rainfall nowcasting using a new parameter — atmospheric electric field (potential gradient, PG) — is investigated. The temporal behavior of the PG measured by a ground-based electric field mill before rainfall was analyzed at the University of Reading Atmospheric Observatory (51.441°N, 0.938°W, 66 m) in 2007–2021 and the Nor Amberd research station (40.367°N, 45.258°E, 2000 m) in 2013–2018. The PG tend to show abnormal oscillations before the precipitation cloud arrived at the PG measuring site. Several rules were applied to determine rainfall and PG oscillation events. Statistical analysis was conducted, and the results showed that PG oscillations occurred before and overlap with 48% (54%) of rainfall events, and PG oscillations occurred in one hour before 68% (77%) of rainfall events started at the University of Reading Atmospheric Observatory site (Nor Amberd research station site). A PG-based rainfall nowcasting model is proposed, which is constructed using machine learning techniques Fully Connected Neural Network and Tree-based Pipeline Optimization Tool. The hit rate, false alarm rate, missed detection rate, TS score of the model reached 93.8% (94.6%), 36% (37.4%), 37.7% (39.3%), 46.1% (44.5%) respectively, which is comparable to that of the state-of-the-art rainfall nowcasting methods. And the heavy rainfall hit rate reached 41.5% (36.4%). PG show a great potential to be used as a new parameter for rainfall nowcasting. And the effect of rainfall nowcasting method that proposed in this paper is barely affected by the weather conditions and the PG measuring range of different sites. Moreover, PG stands out among many other parameters because long-term and stable PG measurements can be obtained at a very low cost using an electric field mill.

Novel Functional Radiomics for Prediction of Cardiac PET Avidity in Lung Cancer Radiotherapy

Traditional methods of evaluating cardiotoxicity focus solely on radiation doses to the heart and do not incorporate functional imaging information. Functional imaging has great potential to improve the ability to provide early prediction for cardiotoxicity for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained as part of standard staging work up for lung cancer patients. Although FDG PET/CT scans are typically used to evaluate the tumor, imaging guidelines note that FDG PET/CT scans are an FDA-approved method to image for cardiac inflammation, and studies have noted that the PET cardiac signal can be predictive of clinical outcomes. The purpose of this work was to develop a radiomics model to predict clinical cardiac assessment of standard of care FDG PET/CT scans. The study included 100 consecutive lung cancer patients treated with radiotherapy who underwent standard pre-treatment FDG-PET/CT staging scans. A clinician reviewed the PET/CT scans per clinical cardiac assessment guidelines and classified the cardiac uptake as: 0 = uniform diffuse, 1 = absent, 2 = heterogeneous, with event rates of 20%, 44%, and 35%, respectively. The heart was delineated and 200 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. We divided the data into an 80% training set and a 20% test set to train and evaluate the classification models. Feature reduction was carried out using the Wilcoxon test (with Bonferroni adjusted p<0.05), hierarchical clustering, and Recursive Feature Elimination. Two automatic machine learning (AutoML) frameworks were used to determine classification models: a Random Forest Classifier (Tree-based Pipeline Optimization Tool, TPOT) and Linear Discriminant Analysis (AutoSklearn). 10-fold cross validation was carried out for training and the accuracy of the ability of the models to predict for clinical cardiac assessment is reported. Fifty-one independent radiomics features were reduced to 3 clinically pertinent features (PET 2D Skewness, PET Grey Level Co-occurrence Matrix Correlation, and PET Median) using feature reduction techniques. The model selected by TPOT showed 89.8% predictive accuracy in the cross validation of the training set and 85% predictive accuracy on the test set. The model selected by AutoSklearn showed 89.7% predictive accuracy in the cross validation of the training set and 80% predictive accuracy on the test set. The novelty of this work is that it is the first study to develop and evaluate functional cardiac radiomic features from standard of care FDG PET/CT scans with the data showing good predictive accuracy with clinical imaging evaluation. If validated, the current work provides automated methods to provide functional cardiac information using standard of care imaging that can be used as an imaging biomarker for early clinical toxicity prediction for lung cancer patients.

Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence

Aerosols play a crucial role in the climate system due to direct and indirect effects, such as scattering and absorbing radiant energy. They also have adverse effects on visibility and human health. Humans are exposed to fine PM2.5, which has adverse health impacts related to cardiovascular and respiratory-related diseases. Long-term trends in PM concentrations are influenced by emissions and meteorological variations, while meteorological factors primarily drive short-term variations. Factors such as vegetation cover, relative humidity, temperature, and wind speed impact the divergence in the PM2.5 concentrations on the surface. Machine learning proved to be a good predictor of air quality. This study focuses on predicting PM2.5 with these parameters as input for spatial and temporal information. The work analyzes the in situ observations for PM2.5 over Singapore for seven years (2014–2021) at five locations, and these datasets are used for spatial prediction of PM2.5. The study aims to provide a novel framework based on temporal-based prediction using Random Forest (RF), Gradient Boosting (GB) regression, and Tree-based Pipeline Optimization Tool (TP) Auto ML works based on meta-heuristic via genetic algorithm. TP produced reasonable Global Performance Index values; 7.4 was the highest GPI value in August 2016, and the lowest was −0.6 in June 2019. This indicates the positive performance of the TP model; even the negative values are less than other models, denoting less pessimistic predictions. The outcomes are explained with the eXplainable Artificial Intelligence (XAI) techniques which help to investigate the fidelity of feature importance of the machine learning models to extract information regarding the rhythmic shift of the PM2.5 pattern.

Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior

Machine learning has been very promising in solving real problems, but the implementation involved difficulties mainly for the inexpert data scientists. Therefore, this paper presents an automated machine learning (AutoML) to simplify and accelerate the modeling tasks. Focused on Python and RapidMiner rapid modeling tools, Tree-based Pipeline Optimization Tool (TPOT) and AutoModel were used. This paper presents a comprehensive comparison between these tools with regard to the prediction accuracy and Area Under Curve (AUC) in classifying real cases of whistleblowing academic dishonesty among undergraduate students of two universities in Indonesia. Additionally, the correlations weight from demographic and Theory of Planned Behavior (TOB) attributes in the different machine learning models are also discussed. All the machine learning algorithms from TPOT and AutoModel are considerable powerful to generate good accuracy level (between 70–93% of AUC) in classifying both cases of whistleblowing and non-whistleblowing on the hold-out samples from the testing process. Generally, based on the validation results of the prediction models, demographic attributes presented more importance than the TBP attributes. The findings of this study will be a great interest of many research scholars to conduct a more in-depth analysis on AutoML for many domains mainly in education and academic misconduct fields.•AutoML is the first of its kind to be empirically compared between TPOT and AutoModel in an application to predict academic dishonesty whistleblowing.•Besides accuracy performances of the AutoML, the proportion of the variance of each attribute from demographic and Theory of Planned Behavior (TPB) is also presented in the prediction models of academic dishonesty whistleblowing.•AutoML is a convenient and reproducible rapid modeling method of machine learning to be used in many kinds of prediction problem.